Method and Apparatus for Environmental Sensing

ABSTRACT

A method for verifying an alarm condition in an environmental sensing system includes monitoring data received from environmental sensors; detecting anomalous data received from one of the plurality of environmental sensors; determining the context in which the anomalous data was acquired is consistent with typical usage activities; and providing an alarm condition alert when the determined context is not consistent with typical activities. A system for intelligently monitoring environmental conditions includes environmental sensors configured to monitor first and second environmental conditions and to communicate with an intelligent analysis module. The intelligent analysis module is configured with logic to determine whether anomalous data has been collected by the environmental sensors, to determine the context in which the anomalous data was collected, to determine whether the anomalous data can be attributed to a routine activity, and to provide an alarm condition if the anomalous data cannot be attributed to a routine activity.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/131,162, filed Mar. 10, 2015, and entitled “METHOD AND APPARATUSFOR ENVIRONMENTAL SENSING,” which is incorporated herein by reference inits entirety and for all purposes.

BACKGROUND

Monitoring environmental conditions such as temperature, humidity, airpressure, light, motion, gas levels, VOC levels, and sound is importantand useful in a number of applications and situations, especially insettings such as scientific laboratories, clinical and medical settings,manufacturing plants, and warehouses. In addition to monitoring ambientconditions in entire rooms, it is also useful to monitor such parametersin smaller spaces such as inside refrigerators, freezers, incubators,storage boxes, fume hoods, and the like. Many sensors that are used tomeasure environmental parameters are designed to measure a singleenvironmental parameter, such as temperature or humidity. Somemonitoring systems can be configured to set off an alarm when a sensor'sreading crosses a given threshold or falls outside of the limits of somenormal operating range. However, these systems provide no insight as towhy the alarm condition was reached. For example, many existingtemperature sensors are configured to raise an alarm or send an alert ifthe temperature crosses, or transgresses, a given threshold withouttaking into account factors such as the duration of the time outside thedesired limits and the extent to which the temperature has changedbeyond a threshold limit. Some temperature sensors are equipped withmore intelligence so that minor transgressions, for example, in durationand extent of temperature change, do not trigger an alarm condition.However, since these alerting and alarm systems generally rely on asingle type of environmental sensor, e.g., a temperature sensor, theyare not able to determine the context in which a transgression may haveoccurred. It is preferable to be able to determine more informationabout why a transgression has occurred and more preferable to be able topredict that a transgression is likely to happen in the future, andfurther more preferable to be able to predict when a transgression islikely to happen. The ability to predict such events can be improvedwhen data from multiple sensors, multiple types of sensors, and multiplesensors of multiple types are combined.

Because existing sensor systems provide no contextual information, ifthe alarm is a true alarm, it is often received too late to takeappropriate action to address the failure. Further, no information isprovided to distinguish false alarms from true alarms, or true alarmsthat are unnecessary or premature because conditions are correctedwithin an acceptable period of time. Thus, there exists a need for moreintelligent sensor systems that can discriminate between fluctuations inenvironmental parameters due to normal usage and activity and those dueto abnormal events, and reduces premature and false or unnecessaryalarms. There is also a need for systems that can predict when anenvironmental parameter will likely go outside of desired limits andadvise a user of a timeframe in which this is expected to occur, so thatcorrective action can be taken.

SUMMARY

In some embodiments, a method for verifying an alarm condition in anenvironmental sensing system includes monitoring data received from aplurality of environmental sensors, detecting anomalous data receivedfrom one of the plurality of environmental sensors, determining, usingthe data received from the other(s) of the plurality of environmentalsensors, whether the context in which the anomalous data was acquired isconsistent with typical usage activities, and providing an alarmcondition alert when the determined context is not consistent withtypical activities. In some embodiments, the method includes predictingwhether the anomalous data is anticipated to exceed defined thresholds,and providing an alarm condition alert when the anomalous data isanticipated to cross defined thresholds; and/or estimating when theanomalous data is anticipated to cross defined thresholds, andcommunicating the estimated time at which the thresholds will becrossed. In some embodiments, determining the context in which theanomalous data was acquired includes monitoring patterns of activityover time to determine normal usage patterns. In some embodiments, whenthe determined context is consistent with typical usage activities, themethod also includes continuing monitoring data received from the one ofthe plurality of environmental sensors; and generating an alarmcondition alert if the anomaly is not resolved within the defined periodof time. The plurality of environmental sensors may include atemperature sensor, a light sensor, a humidity sensor, and/or a motionsensor.

In some embodiments, a system for intelligently monitoring environmentalconditions includes an environmental sensor configured to monitor afirst environmental condition and configured to communicate with anintelligent analysis module, an environmental sensor configured tomonitor a second environmental condition and to configured tocommunicate with the intelligent analysis module, and the intelligentanalysis module is configured to communicate with the environmentalsensors and configured with logic to determine whether anomalous datahas been collected by either of the environmental sensors, to determinethe context in which the anomalous data has been collected, to determinewhether the anomalous data can be attributed to a routine activity and,if the anomalous data cannot be attributed to a routine activity,providing an alarm condition. In some implementations, the system isconfigured to provide the determined contextual information to a user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate high-level system architecture of systems forenvironmental sensing in accordance with aspects of the disclosure.

FIG. 2 illustrates an exemplary sensor device in accordance with aspectsof the disclosure.

FIG. 3 illustrates an exemplary hardware implementation for anintelligent analysis module in accordance with aspects of thedisclosure.

FIG. 4 illustrates a flow chart for determining that an alarm conditionexists, in accordance with aspects of the disclosure.

FIG. 5 illustrates the placement of sensors in an environmentallycontrolled space in accordance with aspects of the disclosure.

FIGS. 6-9 illustrate exemplary changes in temperature and light overtime, in accordance with aspects of the disclosure.

FIG. 10 illustrates exemplary changes in humidity and motion over time,in accordance with aspects of the disclosure.

DETAILED DESCRIPTION

The use of multiple sensors coupled with models to describe expectedbehavior in a given environment can improve the reliability ofmonitoring environmental sensors. Sensors to measure differentenvironmental parameters can be used to monitor a given environment.Such sensors include, but are not limited to sensors that monitor light,temperature, motion, gas, pathogen(s), particles, airborne molecularcontamination, pressure, altitude, air pressure, one or more radiofrequencies, voltage, current, humidity, flow rate, acoustics,radiation, absolute and/or relative position, water quality, atmosphericpressure, orientation, weight and/or mass, and sensors that monitor anycombination of parameters including, but not limited to, those mentionedherein.

It should be appreciated that the aforementioned sensors can beconstructed in a variety of ways and in various combinations. Forexample, a motion sensor can be constructed using different hardwareincluding, but not limited to, an accelerometer that physically moves inresponse to a motion stimulus (such as how a pedometer device operatesto track a person's steps as they walk), an infrared sensor thatmonitors the motion of objects at a distance without being moved itself(such as how some home security motion alarms operate), or a positionsensor such as GPS that monitors position over time. Combining themeasurements from multiple sensors, including multiple sensors of thesame type and/or multiple sensors of different types, provides a morecomplete picture of the activity in an environment. Algorithms and rulesare defined to describe how the measurements from different sensorsshould be analyzed to increase the reliability of the monitoring system.False positives, premature positives, and false negatives are reduced bytaking into account the context in which the sensor measurements occur.

Disclosed herein are methods and systems for verifying alarm conditionsresulting from the monitoring of environmental sensors. Historically,environmental sensing and monitoring have not been widely performed inthe context of how the environment is interacted with. Instead,dedicated sensors have been used to individually monitor singleenvironmental parameters such as temperature, humidity, motion, etc.However, the fluctuation and variation of a single environmentalparameter may depend upon on how things (people, animals, plants,machines, etc.) interact with the environment being monitored, and oftenis correlated with fluctuations and variations in other environmentalparameters, as well. For example, the presence of people can affect thetemperature, humidity, noise, and oxygen levels in a given environment.Thus, monitoring multiple environmental parameters at the same timeprovides insight as to why one or more of the environmental parametersare fluctuating.

In some situations, it is important to know the cause of or reason forthe alarm. Influencing a controlled environment, for example by openingthe door of a refrigerator, incubator, or freezer, could cause atemperature sensor's reading to fall for a relatively short period oftime while an object is being removed from the controlled environment.This is of particular importance, for example, in the case wheretemperature must by tightly controlled for the storage of highlytemperature sensitive materials. However, monitoring only temperatureprovides no information as to why the temperature dropped. Further, whenthe controlled environment is influenced for only a short time in thecontext of routine or typical activity, there is no need to provide analarm condition, as long as the controlled environment returns todefined conditions within a defined period of time. However, if thetemperature within that controlled environment were to changeunexpectedly, for example, because of an unexpectedendothermic/exothermic reaction resulting from accidental contact with areactive chemical compound, this temperature change would not beconsistent with typical activity, and it would be important for a userto be alerted to this change in temperature. Further, it would bebeneficial for a user to be advised if the temperature were expected toexceed defined thresholds that may result in deleterious effects on oneor more items contained within the controlled environment. Stillfurther, it would be beneficial for a user to be advised of when definedthresholds would be breached so that corrective action may be taken, andbreach of the thresholds can be avoided. In some settings, such as alaboratory environment, such advance alerts can help to conserveresources by reducing the need to repeat procedures, experiments, andthe like because of the occurrence of unfavorable environmentalconditions.

The monitoring of environmental sensors also provides valuableinformation outside of a laboratory. For example, in an agriculturalsetting, a combination of acoustic (sound) sensors and air qualitysensors in a dairy pasture may be used to determine that a pattern ofpoor air quality and significant noise from heavy equipment correspondsto road construction adjacent to the pasture. Further analysis mayreveal that, on days that road construction occurs, milk productiondecreases. As a result of this information, the cows can proactively bemoved to another pasture on construction days, so that milk productionis not compromised.

In one implementation, a system in accordance with the present inventioncan determined whether the temperature inside anenvironmentally-controlled or environmentally-monitored space fallsoutside of desired limits due to an actual reportable event, such as apower failure detected via a current sensor or a door to the area notclosing properly detected via motion and/or light sensors, or due totypical activities such as someone opening and closing a door to accesscontents in the area. Examples of such environmentally-controlled orenvironmentally-monitored spaces include incubators, ovens, clean rooms,incubation rooms, laboratory rooms, freezers, refrigerators, walk-inrefrigerators, walk-in freezers, greenhouses, fume hoods, and the like.In such spaces, it is preferable to always keep the temperature withinthe desired limits. However, short term temperature fluctuations outsideof the limits may be acceptable without compromising the quality andintegrity of the contents within the spaces.

In order to better estimate and predict the state of an environment,multiple sensors that measure different environmental parameters can becombined with models that reflect normal or typical activity todetermine when abnormal use case scenarios occur, and to provide contextfor those abnormal use case scenarios. Thus, a system in accordance withaspects of the disclosure can determine if anomalous data from one ormore environmental sensors is a real and reportable event (a true alarmcondition) and provide context for that event, determine if theanomalous result is the result of routine activity or typical activitywhere the system returns to normalcy within an acceptable, definedperiod of time (a premature positive), or determine if the anomalousresult is the result of noise or sensor failure (a false positive).There are many well-known approaches to addressing the issue of noise ina measurement system, such as filtering and time averaging. Adisadvantage of such approaches is that they can incorrectly ignore someevents that should trigger an alert (a false negative). Also, sincefiltering and averaging rely on calculations based on data collectedover a period of time, there can be delays associated with determiningif a threshold has been crossed or not. In the context of the presentdisclosure, noise or sensor failure is addressed by utilizing multiplesensors of the same type.

FIGS. 1A and 1B illustrate high-level system architecture of systems forenvironmental sensing in accordance with aspects of the disclosure. Atleast two sensors 110 are in communication with communication interface115 which transmits data from the sensors 110 to an intelligent analysismodule 130. In FIG. 1A, data can be transmitted directly to intelligentanalysis module 130 via a wired or wireless communication protocol, or acombination therefor. For example, one sensor 110 may be configured totransmit data via a wired Ethernet connection, while the second sensor110 may be configured to transmit data wirelessly via an IEEE802.11-based protocol. The communication protocols used between totransmit data from the sensors may be varied without departing from thescope of the invention and include, without limitation, any wired orwireless networking protocols such as TCP/IP, the aforementioned IEEE802.11-based protocols, cellular communication protocols such as LTE,HSDOA, GPRS, and EVDO, Bluetooth, 60 GHz Protocols such as WirelessHDand WiGig, Rubee, RFID, Wireless Home Automation Protocols including,but not limited to, Insteon, Z-Wave, Zigbee, and BLE, near fieldcommunication (NFC), satellite transmission, and the like.

In FIG. 1B, data is transferred from sensors 110 to a hub 120, whichcollects the data and transmits it to intelligent analysis module 130via a network 125, such as a local area network (LAN), a wide areanetwork (WAN), or the Internet. The implementation illustrated in FIG.1B allows for the intelligent analysis module 130 to be remotely locatedrelative to the sensors 110, and a single intelligent analysis module130 can analyze data for any sensors or groups of sensors that can beconnected with it via a network. Thus, in a large-scale, enterprise-typeapplication, for example monitoring conditions pertaining to perishablegoods in grocery warehouses nation-wide, a cloud-based model may be usedto analyze data collected nation-wide. Computing device 135 may be anycomputing device including, but not limited to, a personal computer,laptop computer, smart phone, personal digital assistant, etc., and maybe used to provide inputs to the intelligent analysis module asrequired, and may be configured to receive alarm condition alerts via anetwork or the Internet when provided by the intelligent analysismodule.

FIG. 2 illustrates an exemplary sensor assembly 200 in accordance withaspects of the disclosure. A “sensor” as referred to herein should beconstrued to include not only sensor hardware/software 210, but aprocessor 215, memory 220, an i/o interface 225 connecting the sensorand processor 215, and a communications interface 205 that allows atleast the transmission of data from the sensor assembly to otherhardware. The sensor assembly 200 should also include sufficient power,whether hardwired or powered via battery or a renewable energy sourcesuch as solar or wind generated power, to power the sensorhardware/software and for communication. Memory 220 stores bothinstructions for execution by processor 215 and at least temporarilystores data acquired by sensor 210, and may be any non-transitorystorage medium including, but not limited to, RAM, ROM, EEPROM, solidstate storage, magnetic disk, removable storage media, or the like.Further, the term processor as used herein may refer to any processor orcombination of processors including, but not limited to,microprocessors, microcontrollers, application specific integratedcircuits (ASICs), programmable logic circuits, digital signal processing(DSP), and the like.

FIG. 3 illustrates an exemplary hardware implementation for anintelligent analysis module in accordance with aspects of thedisclosure. Similar to the sensor assembly 200, intelligent analysismodule 300 includes memory in the form of non-transitorycomputer-readable storage for storing both data and instructions forperforming logic operations on said data, and a processor 315 forexecuting the stored instructions.

FIG. 4 illustrates a flow chart for determining that an alarm conditionexists, in accordance with aspects of the disclosure. Instructions forperforming this process are stored in the memory of the intelligentanalysis module, and executed by the processor of the intelligentanalysis module. Data is received from the plurality of environmentalsensors in operation 410, and monitored in operation 420. In operation430, one or more anomalies are detected in data from one of theplurality of environmental sensors. Anomalies may be specific parametersdefined by a user, or may be fluctuations in data that do not fit thedefined or anticipated pattern of data. In a non-limiting example, inthe case of a temperature sensor, if the temperature is expected to beconstant (within a tolerance), but the data received from thetemperature sensor indicates that the temperature is fluctuating,anomalous data would be detected in operation 430.

In operation 440, data from the other sensors of the plurality ofsensors is analyzed to determine the context in which the anomalous datawas collected. The determination of context may be made usinguser-defined factors, by detecting patterns in the data from the othersof the plurality of sensors, and/or by using adaptive algorithms toidentify events in the data of the others of the plurality of sensorswhich provides at least some insight into what occurred in theenvironment with respect to one or more other environmental sensors whenthe anomalous data was detected. In operation 450, it is determinedwhether the context determined is a typical or routine activity. Typicalor routine activities are identifiable because they are repeatable(generally identifiable by pattern of data), and the sensor readingsgenerally return to normal within a short or predefined period of time.If the anomalous reading can be attributed to or associated with atypical or routine activity, the anomalous data can be disregarded (nocause for alarm condition), so long as the data returns to expectedvalues within a predefined acceptable period of time. If the anomalousdata can not be associated with a typical or routine activity, an alarmcondition is provided because something unexpected has occurred withrespect to the data. An alarm condition can be provided locally on alocally connected or networked computing device, or communicated viae-mail or SMS or other messaging protocol to provide for remotemonitoring capability.

In some implementations, when an alarm condition is provided, furtherlogic may be applied to determine whether or not the anomalous data isexpected to continue, and whether it is expected to exceed pre-definedboundaries or thresholds. In some implementations, modeling techniquessuch as linear regression using a least squares approach, linearextrapolation, exponential functions, logarithmic functions, errorfunctions, polynomial functions, and sigmoid functions may be applied tothe data to predict when a pre-defined threshold value will be exceed,and advance notice can be provided to a user via known messagingprotocols. Thus, a user may have an opportunity to intervene beforeboundary conditions are exceeded or crossed.

While temperature and light sensors are described herein for exemplarypurposes, any combination of environmental sensors could be used withoutdeparting from the scope of the invention. In the example of a walk-inrefrigeration unit, multiple sensors may be set up to capture multipleparameters. A light sensor can be installed inside the unit so that whenthe door is opened, a change in the amount of light can be measured. Ifthe change in the amount of light is greater than a specified threshold,then an alert can be generated. It should be appreciated that the amountof light, or indeed a change in the amount of light, can be measured bymany methods and can be described by different quantities such aslumens, lux, candela, and the like. A light sensor can be a sensor thatis responsive to electromagnetic radiation including, but not limitedto, wavelengths in the visible spectrum, infrared radiation, andultraviolet light. It should be appreciated that other wavelengths ofelectromagnetic radiation, such as radio waves, can also be measured byappropriate sensors.

Multiple light sensors can be installed, and the data from thesemultiple sensors can be combined for analysis. For simplicity, “lightsensor” shall refer to a system that includes at least one sensorconfigured to detect light, and “temperature sensor” shall refer to asystem that includes at least one sensor configured to detecttemperature. It should be appreciated that, in some implementations,multiple sensors of the same type and/or multiple sensors of differenttypes may be co-located, for example, within the same housing orequipment. If a temperature sensor, or a set of multiple temperaturesensors, detects that the temperature inside the unit begins to changeand move towards a defined threshold boundary, and a light sensingsystem also detects a change in the measured light, then one possiblecontextual explanation for the temperature change is that the door ofthe unit has been opened. The following examples illustrate scenarioswhere multi-sensor measurements are useful. It should be appreciatedactual temperature and light readings may be different depending on manyfactors including, but not limited to, sensor location and placementwithin the unit, the sensitivity of sensors, measurement range, and thelike. It should also appreciated that, in some implementations, theintelligent analysis module may be co-located with one or more sensors.

FIG. 5 illustrates an exemplary setting using a typical walk-inrefrigeration unit 500, which is a non-limiting example of anenvironmentally-controlled space. Walk-in refrigeration unit 500includes a door 502 that can be opened and closed to provide access tothe interior space 501. Exemplary placements of temperature sensors 503,505 and light sensors 504, 506 are shown. As shown, light sensor 504 andtemperature sensor 503 are located on the interior face of door 502.Light sensor 506 and temperature sensor 505 are located on an interiorwall of refrigeration unit 500. It should be appreciated that it ispreferable to place a light sensor in a location where it can monitorlight entering the space from the door opening, and that differentplacement locations of light sensors and temperature sensors can affectthe quality and reliability of sensor readings without departing fromthe scope of the invention.

Four exemplary scenarios are provided herein, demonstrating how a usercan benefit from a multiple-sensor detection system by the simultaneoususe of light and temperature sensors. It is to be appreciated that theseare illustrative examples and do not limit the scope of the invention.Sensors that measure other environmental parameters may be used incombination with light and/or temperature sensors, or in place of lightand/or temperature sensors, and environments may be monitored for theenvironmental parameters detected by those other environmental sensors,as well. It should be appreciated that the logic for determining whetheranomalous data exists, for determining context, and for determiningwhether context is typical or routine varies depending on the sensorsused to gather data.

Exemplary Scenario 1: Opening and Closing of Refrigeration Unit Door

FIG. 6 illustrates exemplary changes in temperature and light over time,in accordance with aspects of the disclosure. Specifically, FIG. 6illustrates the changes in light and temperature in a refrigerationunit, such as refrigeration unit 500, over time. Temperature sensorreading 606 and light sensor reading 609 are illustrated as a functionof time. Pre-defined threshold 600 is one boundary limit for temperatureand pre-defined threshold 601 is a second boundary limit fortemperature. Pre-defined threshold 604 is one boundary limit for lightand pre-defined threshold 605 is a second boundary limit for light.Light sensor reading 109 has five sections that correspond to a firstlight level region 611, a second light level region 608, a third lightlevel region 607, a first transition region 112 and second transitionregion 113.

At time 102, logic in the intelligent analysis module detects that thelight level has crossed threshold 104 and continues to light level 108.At approximately the same time, the temperature sensor recordstemperature level 106 rising from a relatively stable baseline value. Attime 110, the temperature crosses threshold 100. At time 103, the lightlevel crosses threshold 104 to return back within the threshold limits104 and 105. At time 114, the temperature level also drops down toreturn between desired threshold levels 100 and 101.

Logic in the intelligent analysis module determines that the light levelreturned back to its nominal baseline level at time 103, therebyproviding contextual information that the door was likely opened atapproximately time 102, and closed at approximately time 103. Thispattern of data is consistent with the routine activity of opening thedoor of the refrigeration unit. The alerting algorithm determines that,because this is a routine activity, there is no need for prematurealarm. The temperature would be expected to return back to the desiredrange within a short while, thereby avoiding the triggering of apremature alarm. In some implementations, the alerting algorithm checksagain at one or more later points in time to confirm that thetemperature did indeed return back to the desired range. An alert cansubsequently be sent if the temperature does not return to the desiredrange.

Exemplary Scenario 2: Quick Opening and Closing of Refrigeration UnitDoor

FIG. 7 illustrates another example scenario of how temperature in arefrigeration unit can change over time. Temperature sensor reading 706and light sensor reading 709 are shown as a function of time. Threshold700 is one boundary limit for temperature and threshold 701 is a secondboundary limit for temperature. Threshold 704 is one boundary limit forlight and threshold 705 is a second boundary limit for light. Lightsensor reading 709 has four sections that correspond to a first lightlevel region 708, a first transition region 710 and second transitionregion 711, and a second light level region 707 that corresponds toapproximately the same light level value at region 708.

This example scenario represents a situation where the door is quicklyopened approximately at time 702 and closed at time 703. The temperature706 rises slightly between time 702 and 703 but does not cross threshold700. However, it is beneficial to understand that the temporary rise intemperature 706 between time 702 and time 703 was due to a recognizable,attributable activity (such as a door opening and closing, as indicatedby the light level changing in regions 710 and 711) that is detectableby logic in the intelligent analysis module, and not due to some otherundiagnosed malfunction of the refrigeration unit.

FIG. 8 illustrates another example scenario of how temperature in therefrigeration unit can change over time when the door is not fullyclosed. Temperature sensor reading 806 and light sensor reading 809 areshown as a function of time. Threshold 800 is one boundary limit fortemperature and threshold 801 is a second boundary limit fortemperature. Threshold 804 is one boundary limit for light and threshold805 is a second boundary limit for light. Light sensor reading 809 hasfive sections that correspond to one light level region 811, a secondlight level region 808, a third light level region 807, a firsttransition region 812 and second transition region 813.

At time 802, logic in the intelligent analysis module detects that thelight level has crossed threshold 804 and continues to light level 808.At approximately the same time, the temperature sensor recordstemperature level 806 rising from a relatively stable baseline value. Atapproximately time 803, the light level falls toward threshold 804 butdoes not return back within the threshold limits 804 and 805; instead,it returns to light level 807 that is slightly higher than threshold 804but is less than its previous level of 808. The temperature dropsslightly between time 814 and time 810, and then it gradually increasesafter approximately time 810 (section 816) and ultimately crossesthreshold 800 at approximately time 815 (section 817).

In this scenario, the gradual temperature rise 816 may not be detecteduntil threshold 800 is crossed by simple temperature monitoring systemsthat just rely on measuring the temperature alone. However, bymonitoring the light sensor as well, it is clear that the door wasopened for a period of time between time 802 and time 803, but that bytime 814, the door did not fully close, thereby allowing some level ofexternal light into the unit. Thus, logic in the intelligent analysismodule would interpret the rising temperature signal 816 in combinationwith increased light level 807 above threshold 804 as resulting from thedoor not being fully closed, which is not a typical activity for arefrigeration unit. Furthermore, taking into account light level 808prior to light level 807, logic in the intelligent analysis module couldfurther deduce that a user more fully opened the door and that the doordid not fully close after use. With this information, logic in theintelligent analysis module can conclude that, since the door is notfully closed, the temperature will continue to rise to a new higherlevel. Based on the rate of increase of the temperature 816, it would bepossible to estimate when the temperature level will cross threshold800, and provide advance notice to the user of this estimate.

The ability to estimate when in the future the temperature inside theunit will cross a threshold level is important and useful. By triggeringan alarm or alert a period of time before the temperature is expected tocross a threshold level, a notified user can intervene to investigatethe cause of the temperature rise and take action to fix the cause ofthe unwanted temperature rise, or take other action to reduce the impactof the temperature rise, such as moving the contents of the compromisedunit to another functional unit.

In the example of FIG. 8, the temperature is rising in an approximatelinear fashion from time 810. One method of estimating the time at whichthe temperature will cross threshold 800 is to use a linear equation asfollows:

-   -   Temp(t)=Temperature Sensor Reading at time t (shown by line 806)    -   Threshold1=Upper Temperature Threshold Level (shown by dashed        line 800)    -   Threshold2=Lower Temperature Threshold Level (shown by dashed        line 801)    -   tRise=time 310 when temperature starts to increase    -   tcrossing=estimated time at which the temperature will cross        threshold 300    -   K=slope of temperature versus time data estimated from data in        region 316

Temp(t)=K*(t−tRise)+Temp(tRise)

so for: t=tcrossingTemp(tcrossing)=Threshold)

so: Temp(tcrossing)=K*(tcrossing−tRise)+Temp(tRise)

thus:

Threshold1=K*(tcrossing−tRise)+Temp(tRise)

tcrossing=(Threshold)−Temp(tRise))/K+tRise

It may be necessary to estimate the value of K to perform the abovecalculations. There are many well-known techniques to estimate a slopefrom given data, such as linear regression using a least squaresapproach. The above example can be better illustrated with a numericalexample with the following assumptions:

Temp(t)=Temperature Sensor Reading at time t (shown by line 306)

Threshold)=−10 C Threshold2=−15 C

tRise=3:00 AM

Temp(3:00 AM)=−12

Ctcrossing=estimated time at which the temperature will cross threshold300

K=0.1 C/min

tcrossing=((−10 C)−(−12 C))/(0.1 C/min)+3:00 AMtcrossing=(2 C)/(0.1 C/min)+3:00 AMtcrossing=20 min+3:00 AMtcrossing=3:20 AMThus, the intelligent analysis module can send an alert to a user viaemail, SMS, or any other messaging protocol, to indicate that thetemperature is estimated to cross the upper threshold limit 300 atapproximately 3:20 AM before that time has been reached.

It should be appreciated that a linear extrapolation model is only oneof many possible forms of equation for modeling and predicting a futurevalue of temperature and/or the time in the future when the temperatureis expected to reach a particular level based on current and previousvalues. Other forms include exponential functions, logarithmicfunctions, error functions, polynomial functions, and sigmoid functions.

One method of estimating if and/or when the temperature will go outsideof the desired limits is to fit a regression line to data that hasoccurred in the immediate past, for example the last 15 or 30 minutes.For example, one can use a linear regression. This would result in agradient describing the change in temperature over time (for example,0.2 C/minor 1.5° F./sec). One can perform this type of calculation inreal time, or at pre-defined intervals, for example, once every 5minutes, once every 10 minutes, once every 30 minutes, or once everyhour. This calculation can also be performed when sensors pick upincreased activity, such as when the door is opened or closed. Forsituations when a pre-defined model for describing the change in theenvironmental parameter (in this example, temperature) is not readilyavailable, one can perform statistical analysis on the data, such ascomputing the mean, median, and standard deviation of the data. A movingaverage calculation can also be performed, for example a moving averageof the previous 5 or 10 minutes. Here are some examples of how themoving average and gradient calculations can be used:

-   -   If the gradient is approximately zero (or the mean value of the        gradient over a period of time is approximately zero), then the        environmental parameter is expected to approximately maintain        its current value.    -   If the gradient is less than zero (or the mean value of the        gradient over a period of time is less than zero), then the        environmental parameter is expected to decrease in value over        time.    -   If the gradient is greater than zero (or the mean value of the        gradient over a period of time is greater than zero), then the        environmental parameter is expected to increase in value over        time.    -   If the moving average and the gradient both continue to increase        in value, then the calculation suggests that the environmental        parameter will continue to increase.    -   If the moving average and the gradient both continue to decrease        in value, then the calculation suggests that the environmental        parameter will continue to decrease.    -   If the gradient is approximately constant and non-zero for a        period of time, then a linear approximation can be used to        extrapolate when in the future the measured environmental        parameter is likely to cross a threshold limit and an alert can        be sent to a user with the estimated threshold crossing time.    -   If the gradient is increasing with time, then a nonlinear model        may be used to approximate the time in the future when the        measured environmental parameter is likely to cross a threshold        limit and an alert can be sent to a user with the estimated        threshold crossing time.    -   If both the moving average and the gradient are increasing (or        decreasing) with time and moving away from a nominal baseline        value towards a threshold limit, then an alert can be sent to        notify a user. An alerting or alarm algorithm can further        comprise logic to only include valid data points in calculations        and disregard invalid data points. Invalid data points can be        data points that were measured during known interaction or usage        events, such as a user opening a door.

FIG. 9 illustrates another example scenario of how temperature in therefrigeration unit can change over time when a real fault occurs.Temperature sensor reading 906 and light sensor reading 909 are shown asa function of time. Threshold 900 is one boundary limit for temperatureand threshold 901 is a second boundary limit for temperature. Threshold904 is one boundary limit for light and threshold 905 is a secondboundary limit for light. Light sensor reading 909 remains constant forthe duration of this scenario. At time 902, the temperature 906 beginsto increase. At approximately time 903, the temperature crossesthreshold 900 and continues to increase.

In this scenario, the light level 909 remains constant, so logic in theintelligent analysis module is not able to determine any contextualinformation to explain the temperature increase. Thus, the logic in theintelligent analysis module concludes the measured increase intemperature starting from time 902 is likely not due to normal activitysuch as opening of the door. An alerting algorithm would thereforeclassify the rising temperature and the crossing of threshold 900 attime 903 as real, alarm-worthy events, rather than a false positive, andsend an alert.

Although the above examples relate to temperature and lightmeasurements, one ordinarily skilled in the art will recognize that asimilar approach can be used for other types of sensors as well. Forexample, one can measure the opening and closing of the door using anaccelerometer mounted on the door or with a magnetic sensor thatmonitors when the door is open or closed. It is also possible to monitormotion using motion detectors, similar to ones commonly used in homesecurity systems (such as the Honeywell RCA902N1004/N Wireless MotionDetector, SimpliSafe, and GE Choice Alert). Such motion detectors candetect when the door is opened or closed or when there is activityinside the unit. Acoustic sensors can be used to determine when a dooris opened or closed, or when there is activity inside a unit or near anarea.

In addition to temperature, it is often desirable to measure otherenvironmental parameters, such as humidity and noise. In these cases, itis possible to monitor additional environmental parameters such asmotion and light to determine if the fluctuations in humidity and noiseare due to normal expected activity. For example, an increase inhumidity in a low-humidity environment such as a manufacturing cleanroom can be correlated with an increase in motion and/or noise,signifying the presence of people, as is illustrated by the followingexample.

FIG. 10 illustrates one example scenario of how humidity in the unit canchange over time. In this example, a humidity sensor and a motion sensorare placed within a confined space. The motion sensor in this example isone that detects motion within a given space. Humidity sensor reading1006 and motion sensor reading 1009 are shown as a function of time inFIG. 10. Threshold 1000 is one boundary limit for humidity and threshold1001 is a second boundary limit for humidity. Threshold 1004 is oneboundary limit for motion and threshold 1005 is a second boundary limitfor motion. Motion sensor reading 1009 has five sections that correspondto one motion level region 1011, a second motion level region 1008, athird motion level region 1007, a first transition region 1012 andsecond transition region 1013.

At time 1002, logic in the intelligent analysis module detects that themotion level has crossed threshold 1004 and continues to motion level1008. At approximately the same time, the humidity sensor recordshumidity level 1006 rising from a relatively stable baseline value,which is also detected by logic in the intelligent analysis module. Attime 1010, the humidity crosses threshold 1000. At time 1003, the motionlevel crosses threshold 1004 to return back within the threshold limits1004 and 1005. At time 1014, the humidity level also drops down toreturn between desired threshold levels 1000 and 1001.

Data from the motion sensor, interpreted by logic in the intelligentanalysis module, makes it would be clear that at time 1003 the motionlevel returned back to its nominal baseline level, thereby indicatingthat a person entered the space approximately time 1002 and exited atapproximately time 1003. Logic in the intelligent analysis module woulddetermine that the door was then closed and that the humidity would beexpected to return back to the desired range within a short while,because this is a routine or typical activity, thereby avoiding thetriggering of a false alarm. Logic in the intelligent analysis modulewould check again at a later point in time to confirm that the humiditydid indeed return back to the desired range. An alert can subsequentlybe sent if it the humidity did not return to the desired range.

It can also be appreciated that multiple different types of sensorreadings can be analyzed in combination to determine a morerepresentative and reliable state of the environment. For example, alight sensor can be combined with humidity and motion sensors to furtherindicate events such as opening and closing of a door beyond just themotion sensor's reading being used to determine when a person is insidethe unit. Another example is to monitor noise or sound levels. Thepresence of a person and his/her activity within the unit may createsounds that can be measured by a sound sensor.

Furthermore, more intelligent algorithms can be constructed that takeinto account information in addition to sensor readings. As is often thecase, patterns of usage can vary with certain patterns. For example, itis expected that usage patterns during weekdays (Monday to Friday) willbe different than usage patterns on weekends (Saturday and Sunday).Other days of the week such as holidays (e.g. Christmas) are alsoexpected to have different usage patterns than other typical days.Furthermore, different times of the day can also experience differentusage patterns: during typical office hours (from 9 am until 5 pm)people will likely interact with the environmental units more frequentlythan during the night time hours. Such time-based usage patterns can beincorporated into logic in the intelligent analysis module to increaseor decrease the sensitivity and alert trigger conditions, and to updateroutine activity patterns. For example, during regular working hours,the threshold limits for temperature might be widened, but duringnon-working hours, these limits might be narrowed. One ordinarilyskilled in the art will recognize that it is possible to alter thealgorithm and the triggering conditions based on time of day and day ofthe week/month/year to tailor conditions for expected usage patterns.

In some situations, activities in a given environment may not be regularand easily described by time of day or by day of the week/month/year.For such situations, an adaptive algorithm can be used by the logic ofthe intelligent analysis module, whereby the activity patterns for aprevious period of time are used to denote typical or normal usagepatterns. For example, a system can be implemented to monitor typicalactivity behavior patterns such as activity by time of day and by day ofweek/month/year, frequency of usage and interaction with anenvironmental unit and the like. This can be seen as a training periodfor logic in the intelligent analysis module to learn typical patternsof usage and behavior which can serve as a basis for adjusting thesensitivity of an alert or alarm algorithm or the threshold limits.

What is claimed is:
 1. A method for verifying an alarm condition in anenvironmental sensing system, comprising: monitoring data received froma plurality of environmental sensors; detecting anomalous data receivedfrom one of the plurality of environmental sensors; determining, usingthe data received from the other(s) of the plurality of environmentalsensors, whether the context in which the anomalous data was acquired isconsistent with typical usage activities; and providing an alarmcondition alert when the determined context is not consistent withtypical activities.
 2. The method according to claim 1, furthercomprising predicting whether the anomalous data is anticipated toexceed defined thresholds, and providing an alarm condition alert whenthe anomalous data is anticipated to cross defined thresholds.
 3. Themethod according to claim 1, further comprising estimating when theanomalous data is anticipated to cross defined thresholds, andcommunicating the estimated time at which the thresholds will becrossed.
 4. The method according to claim 1, wherein determining thecontext in which the anomalous data was acquired comprises monitoringpatterns of activity over time to determine normal usage patterns. 5.The method according to claim 1, wherein typical usage activities varyby day or time.
 6. The method according to claim 1, further comprising,when the determined context is consistent with typical usage activities,continuing monitoring data received from the one of the plurality ofenvironmental sensors; and generating an alarm condition alert if theanomaly is not resolved within the defined period of time.
 7. The methodaccording to claim 1, wherein the plurality of environmental sensorsinclude a temperature sensor and a light sensor.
 8. The method accordingto claim 1, wherein the plurality of environmental sensors include ahumidity sensor and a motion sensor.
 9. A system for intelligentlymonitoring environmental conditions, comprising: an environmental sensorconfigured to monitor a first environmental condition and configured tocommunicate via a wired or wireless network; an environmental sensorconfigured to monitor a second environmental condition and to configuredto communicate with an intelligent analysis module; and an intelligentanalysis module configured to communicate with the environmental sensorsand configured with logic to determine whether anomalous data has beencollected by either of the environmental sensors, to determine thecontext in which the anomalous data has been collected, to determinewhether the anomalous data can be attributed to a routine activity and,if the anomalous data cannot be attributed to a routine activity,providing an alarm condition.
 10. A system according to claim 9, whereinthe system is configured to provide the determined contextualinformation to a user.